A financial analysis of four carbon offset accounting protocols for a representative afforestation project (southern Ontario, Canada)
Bibliographic record
Abstract
Forestry projects participate in carbon markets by sequestering carbon dioxide equivalent (CO2e) and producing carbon offsets. The creation of forest-based offsets is guided by protocols that dictate how sequestered CO2e is converted into marketable offsets. Existing protocol designs aim to produce offsets that meet sustainability requirements, while providing financial incentives for landowner participation. However, limited Canadian uptake implies that current financial incentives are insufficient to encourage the production of carbon offsets via private landowners. Here we consider various design features of four protocols and their financial implications for an illustrative afforestation project in southern Ontario, Canada. We explore the protocols (two tonne–tonne protocols and two tonne–year protocols) under two afforestation project management systems (“no-harvest” and “harvest” management scenarios). Results indicate that a project that terminates in a harvest is not economically attractive at current CO2e prices under any protocol design at a scale likely to be undertaken in southern Ontario, Canada. Projects that do not conclude in harvest are generally more attractive. Tonne–tonne protocols that pay upfront for sequestered CO2e improve the economic attractiveness of afforestation projects, but the delayed realization of the value of offset credits under tonne–year protocols reduces the economic attractiveness of these projects. We discuss these results in light of the choices facing afforestation project proponents and offset protocol designers (including governments) in general, and provide detailed insights into the financial dynamics of the Canadian case.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".